Norma ISO/IEC 27002

Figure 17 shows the milk efficiency tree obtained from the regression of DEA estimates on all 90 discretionary and non-discretionary inputs and ratios contained in the TodoagroBase (refer Appendix 2). The diagram shows 11 terminal nodes on two major branches split by the root variable, litres of milk per cow (L/Cow), at a threshold value of 4,664 L.

The root variable, litres per cow, split the sample into right and left branches. The right branch accounted for more than two thirds of the sample, clustering those DMUs producing more than 4,664 L/cow and year. One virtually efficient class (0.954, n = 235) was drawn as the result of a second split made by the ratio wages paid per litre of milk. The threshold value was CLP$16.22/L, equivalent to NZ$0.4123 per 10 L of milk24. The second best efficiency class (0.9375; n=236) in this branch had a higher wages ratio and used less than 295.7 g/L of supplements, but almost offset these with a gross farm revenue per cow greater than CLP$1.087 million, equivalent to NZ$2,764 per cow. These two large classes made up almost half of the total sample.

The root variable split just over one third of the Chilean sample into the left branch. The least efficient class (n = 56) was found on this branch at the end of the outer pathway. It had an average efficiency of 0.7228, which resulted from the combination of lower production per cow, a higher than the threshold value for production costs and wages, and also a higher total cost, including rent and interests. The second split on this branch was the ratio production costs per litre of milk, which is a Chile-specific variable similar to the well-known farm working expenses in New Zealand (see definition Chapter II). The critical value was CLP$121.80/L, equivalent to NZ$3.10 per 10 L of milk. The third split was again the wages ratio at the threshold value of CLP$17.79/L, equivalent to NZ$0.45 per 10 L of milk produced.

Following the same reasoning as with the New Zealand sample, a modification was carried out to expose any hidden relationships among the less strong variables. Figure 18 shows a second tree generated by using the same DEA scores based on the milk output, with six milk-related ratios excluded from the dataset (the analysis regressed DEA estimates on 84 variables).

23 _{Based on an exchange rate of CLP$394.65 per NZ$.}

24 _{Based on the assumption that 10 litres of Chilean milk (3.4% Protein, 3.7% Fat) would be roughly}

Figure 18 revealed new relationships, but these were fewer than expected. Only two different variables came out compared to those in Figure 17: operating profit per cow, and litres produced per milked cow. It is worth clarifying that ‘milked cows’ are the average number of cows effectively milked throughout the year, which is different from ‘total cows’ which includes milked, pregnant, and empty cows on the farm. These new variables appeared at the third and fourth levels, respectively. The root variable changed from L/cow, which was exempt from available variables, to gross farm revenue per cow, confirming a strong association between these two ratios.

The right branch clustered 625 observations that had gross farm revenue per cow higher than CLP$956,500, equivalent to NZ$2,424. The second level split was made by the ratio Supplement fed per litre of milk, at the threshold value of 295.3 g/L. DMUs using lower levels of supplement were further split to the outer side. Following this pathway, operating profit created the highest efficiency class (0.9572, n = 251), exhibiting a profit per cow greater than CLP$303,200, equivalent to NZ$768/cow. The third highest efficiency class (0.9254) comprised those 184 observations that had a lower profit than the threshold and also produced more than 6,574 L/milked cow.

The root variable also split 403 observations to the left branch. Surprisingly, the second best efficiency class (0.9472) was located on this branch, at the end of a very different pathway than that followed to reach the best performing leaf. This second best class had a lower revenue per cow than the threshold, but this was offset by a lower than CLP$13.39 or NZ$0.34 per 10 L value for wages per litre of milk produced. In contrast, the lowest efficiency class also had a lower revenue per cow, but a much higher value of wages per litre at CLP$20.44 or NZ$0.52 per 10 L; this infers that paid labour was 53% less productive on these low efficient observations. This class was also affected by higher direct costs of production, and again, GFR CLP$/cow. Table 37 highlights the order and frequency of appearance of the variables exposed by both trees based on milk DEA results, from root to fourth level split as revealed by Figures 17 and 18.

Table 37

Key Performance Indicators (KPIs) as Revealed by MS Trees

Benchmarks, such as L/Cow, GFR/cow, Supplement g/L, and Wages/L, appeared as very strong variables at the first, second, third, and fourth levels, and repeatedly played a role in the physical–oriented trees. The structure of these trees is more or less similar to that found for the New Zealand trees. The milk production ratio per cow consistently appeared on top fo the tree, and labour, expenditure, and profit also came out as strong variables.